similar to: getting the normal dist from the chisqr with 1df

hie how do you compute the logrank test using R what commands do you use my data looks something like just an example treatmentgrp strata censoringTime survivalTime censoring act.surv.time [1,] 2 2 42.89005 1847.3358 1 42.89005 [2,] 1 1 74.40379 440.3467 1 74.40379 [3,] 2 2

hie l would like to create a 6th column "actual surv time" from the following data the condition being if censoringTime>survivaltime then actual survtime =survival time else actual survtime =censoring time the code l used to create the data is s=2 while(s!=0){ n=20 m<-matrix(nrow=n,ncol=4)

hie R users l have the following matrix n=20 m<-matrix(nrow=n,ncol=4) colnames(m)=c("treatmentgrp","strata","survivalTime") for(i in 1:n) m[i,]<-c(sample(c(1,2),1,replace=TRUE),sample(c(1:2),1,replace=TRUE),rexp(1,0.07),rexp(1,0.02))

l have the following dataset and would like to calculate the actual survival time by if censoring time > survival time then actual survival time =survival time else its= censoring time. treatmentgrp strata censoringTime survivalTime censoring actualsurvivaltim [1,] 1 1 1.012159 1137.80922 0 [2,] 2 2

l would like to create the following matrice treatmentgrp strata 1 1 1 1 1 1 1 2 1 2 1 2 2 1 2 1 2 1 2 2 2 2 2 2 l should be able to choose the size of the treatment grps and stratas the method l used intially creates the

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i would like to add a variable to an existing matrix by manipulating 2 previous variables eg for the data m treat strata censti survTime [1,] 1 2 284.684074 690.4961005 [2,] 1 1 172.764515 32.3990335 [3,] 1 1 2393.195400 24.6145279 [4,] 2 1 30.364771 8.0272267 [5,] 1 1 523.182282 554.7659501 l

how do l programme the logrank test. l am trying to compare 2 survival curves --------------------------------- [[alternative HTML version deleted]]

hie is it possible to get the code for the logrank test l just need the chisquared value. thanks --------------------------------- [[alternative HTML version deleted]]

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =

Dear useRs, There is a new version 0.1-2 of the package surv2sample available on CRAN. Users of the previous versions should update because a bug in the function cif2.ks has been fixed. General information about the package: surv2sample provides various two-sample tests for right-censored survival data. Three main areas and corresponding methods are: * comparison of two survival

Dear useRs, There is a new version 0.1-2 of the package surv2sample available on CRAN. Users of the previous versions should update because a bug in the function cif2.ks has been fixed. General information about the package: surv2sample provides various two-sample tests for right-censored survival data. Three main areas and corresponding methods are: * comparison of two survival

Hello! I want to compare two Kaplan-Meier-Curves by using the Logrank-Test: logrank(Surv(time[b], status[b]) ~ group[b]) This way I only get the value of the test-statistic, but not the p-value. Does anybody know how I can get the p-value? Thanks in advance! Verena Hoffmann

Hi All, ? I am trying to use the lordif package in R and can't figure out the reason for the above error when I try to run the calctheta() function. ################## # read data using foreign package - must be Stata 11 or earlier racedata<- read.dta("race.dta")??? # item responses resp.data<-racedata[,-c(1:2)] # grouping variable is in the second column

Dear list,I am quite new to the world of biostatistics and I encounter some issues in the precise understanding of the coxph function of the survival package.I have a set of survival data (patient who had (or died from) a breast cancer) I'd like to see which are the variables that might cause dead or not.When trying> summary(coxph(Surv(Time_to_distant_recurrence_yrs, !Distant_recurrence)~

Hi, I have a text mining project and currently I am working on feature generation/selection part. My plan is selecting a set of words or word combinations which have better discriminant capability than other words in telling the group id's (2 classes in this case) for a dataset which has 2,000,000 documents. One approach is using "contrast-set association rule mining" while the

I have just uploaded a new package called interval. It does weighted logrank tests (e.g., logrank, Wilcoxon-type tests) on interval censored data. It allows Surv objects and can calculated exact p-values. It calls the perm package, but also allows calculation of scores that may be output to faster permutation packages like coin. There is a full description of the packge and methods in a

I have just uploaded a new package called interval. It does weighted logrank tests (e.g., logrank, Wilcoxon-type tests) on interval censored data. It allows Surv objects and can calculated exact p-values. It calls the perm package, but also allows calculation of scores that may be output to faster permutation packages like coin. There is a full description of the packge and methods in a

DeaR experts, I was asked for a log-rank pairwise survival comparison. I've a straightforward way to do this using the SAS system: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#/documentation/cdl/en/statug/63033/HTML/default/statug_lifetest_sect019.htm What I've found in R is shown below, but it's not a logrank test, I suppose. (The documentation

Hi all, I have uploaded an update of the interval package, which calculates nonparametric maximum likelihood estimates (NPMLE) of distributions and exact or asymptotic weighted logrank-type tests for interval censored data. An important part of the update is that now the default initial estimate of the NPMLE uses the MLEcens package and is much faster. This increases the speed of the logrank-type